AI Research

Open and Emergent Problems in Agentic Privacy and Security: A Contextual Angle

Generative AI

Computer Vision: SIFT algorithm (Scale Invariant Feature Transform)

Elegant object matching from several viewpoints The post Computer Vision: SIFT algorithm (Scale Invariant Feature Transform) appeared first on Towards Data Science.

What the Kyoto Vision Means for AI-Powered Science

Seventeen countries have endorsed the Kyoto Vision for AI-powered science. Its push toward autonomous discovery raises questions about computing access and how governments will turn policy ambitions into research…

What If AI Is a Phase in Earth’s Evolution?

Philosopher Benjamin Bratton has a much bigger way of thinking about AI: as the newest step in a billions-of-years process where matter produced life, life produced intelligence, and intelligence learned to build…

How to Build a Cheap, Yet Reliable Model Router With Jev

Model routing can finally be a design choice rather than a system enhancement The post How to Build a Cheap, Yet Reliable Model Router With Jev appeared first on Towards Data Science.

😺 Trump named an AI czar

What Nvidia and AMD’s AI Acquisitions Say About the Next Chip War

Nvidia’s planned Hugging Face acquisition and AMD’s World Labs deal bring both chipmakers closer to the developers and researchers shaping future AI workloads, giving them an earlier view of how compute requirements may…

When May a Bandit Leave Its Anchor? E-Process-Authorized Thompson Sampling under Non-stationarity

arXiv:2610.03646v1 Announce Type: new Abstract: Stationarity rewards memory, but after a change the same history can mislead. We ask when forgetting should be permitted. E-process-authorized Thompson sampling (e-ATS)…

A Polynomial-Time Algorithm for Variational Inequalities under the Minty Condition

arXiv:2504.03432v4 Announce Type: replace-cross Abstract: Solving (Stampacchia) variational inequalities (SVIs) is a foundational problem at the heart of optimization. However, this expressivity comes at the cost of…

Electronic Density versus Geometry for Machine-Learned Molecular Absorption Spectra

arXiv:2610.03444v1 Announce Type: cross Abstract: Molecular optical absorption spectroscopy provides a direct probe of electronic structure and is widely used for molecular identification, interpretation of…

From Behavior to Provenance: Attributing Tabular Foundation Models to Synthetic Pretraining Data

arXiv:2610.02347v1 Announce Type: new Abstract: Training-data attribution aims to identify which training examples shape model behavior, yet validating such claims is difficult because causal training influence is…

Trade-off Functions for DP-SGD with Subsampling based on Random Allocation: Tight Upper and Lower Bounds

arXiv:2605.06259v3 Announce Type: replace Abstract: Within the $f$-DP framework, we derive a tight analysis of the trade-off function for Differentially Private Stochastic Gradient Descent (DP-SGD) with subsampling…

Geometric Data Perturbation with Noisy-Anchor Alignment for Privacy-Preserving Collaborative Learning

arXiv:2608.18749v3 Announce Type: replace Abstract: Geometric data perturbation enables one-shot representation sharing for privacy-preserving collaborative learning: each participant applies a secret…

Learning to Price Electricity for Optimal Demand Response

arXiv:2610.00755v2 Announce Type: replace-cross Abstract: There is considerable interest in using time-varying electricity prices to shape consumer demand response, and better align energy demand with renewable…

Planning to Learn

arXiv:2610.03667v1 Announce Type: new Abstract: Policy-gradient methods are central to modern reinforcement learning, including LLM post-training. When they struggle, the usual suspects are exploration, credit…

RMCW: A Deletion-Robust Watermark Based on Reed--Muller Codes for Language Models

arXiv:2610.02817v1 Announce Type: cross Abstract: Large Language Model (LLM) watermarking provides a lightweight mechanism for identifying text generated by a specific model, but its robustness remains fragile under…

Getting Your Guidance Weights Right in diffusion and flow-matching posterior sampling

arXiv:2610.03503v1 Announce Type: new Abstract: Training-free posterior sampling methods, also known as Plug-and-Play methods, leverage pretrained unconditional diffusion or flow-matching models to solve inverse…

Can SGD Handle Heavy-Tailed Noise?

arXiv:2508.04860v2 Announce Type: replace-cross Abstract: Stochastic Gradient Descent (SGD) is a cornerstone of large-scale optimization, yet its theoretical behavior under heavy-tailed noise -- common in modern machine…

Probe the Harness: Setup Checks for Stale-Data RL Comparisons in Language Models

arXiv:2610.02911v1 Announce Type: new Abstract: Methods for training language models on stale samples are judged by comparisons against importance-corrected baselines. We show that details of the experimental harness…

Recoverability Has a Law: The ERR Measure for Tool-Augmented Agents

arXiv:2601.22352v2 Announce Type: replace Abstract: Language model agents often appear capable of self-recovery after failing tool call executions, yet this behavior lacks a formal explanation. We present a predictive…